train

name	train
description	Train a first NEP potential from labeled extxyz data. Use when the user needs `nep.in`, `train.xyz`, `test.xyz`, parameter guidance, loss.out interpretation, or deployment of the resulting `nep.txt` back into GPUMD. NEP is the native machine-learning potential for GPUMD and plays the role that DeePMD plays for LAMMPS.
compatibility	Requires a NEP-capable GPUMD build with the `nep` executable and labeled training/test extxyz datasets.
catalog-hidden	true
license	GPL-3.0-only
metadata	{"author":"Jhin","version":"0.2.0"}

NEP Train

Train a baseline NEP potential from labeled data. This is the general-purpose training subskill for the energy + force (+ virial) model. For dipole or polarizability auxiliary properties, the same nep executable is used but with a different model_type — see the auxiliary-properties reference.

Quick start

nep

The nep binary reads nep.in, train.xyz, and (optionally) test.xyz from the current directory and writes nep.txt, loss.out, and the parity files. Some HPC builds wrap this as srun -n 1 --gpus=1 nep or equivalent — ask the user.

Agent responsibilities

1. Confirm the environment has the nep executable and that the user has supplied labeled training data.
2. Collect the minimum required information:
   • labeled training dataset path(s) (train.xyz)
   • labeled test dataset path (test.xyz, optional but recommended)
   • full species list in the exact order the user wants in type
   • target observable(s) for post-training validation
3. Validate dataset headers before training:

   python ../scripts/validate_extxyz_headers.py train.xyz --mode train
   python ../scripts/validate_extxyz_headers.py test.xyz --mode train
   

4. Write nep.in yourself and explain the hyperparameters that matter.
5. Run training and monitor loss.out.
6. Deploy the trained nep.txt back into a GPUMD sanity MD run before calling the training "done".

Workflow

Step 1. Prepare datasets

train.xyz and test.xyz are concatenations of labeled extxyz frames. Each frame must contain:

• atom count line
• header with Lattice="...", energy=..., Properties=species:S:1:pos:R:3:force:R:3
• N rows of species x y z fx fy fz

Optional but important header fields:

• virial="..." — full 9-component virial if stress-fit is desired
• stress="..." — some converters write stress instead of virial; be explicit
• config_type=... — free-form label used for per-group weights
• weight=... — frame-level loss weight

Minimal frame:

2
Lattice="5.43 0 0 0 5.43 0 0 0 5.43" pbc="T T T" energy=-10.860000 Properties=species:S:1:pos:R:3:force:R:3
Si 0.000000 0.000000 0.000000  0.0010 -0.0020  0.0030
Si 1.357500 1.357500 1.357500 -0.0010  0.0020 -0.0030

Keep units consistent:

• positions in Å
• total energy in eV (not per atom)
• forces in eV/Å
• virial/stress: confirm sign convention and whether the header carries the full tensor or a normalized quantity

For help generating these files from DFT output, see references/labels-from-dft.md.

Step 2. Write nep.in

Annotated baseline (see assets/examples/baseline/nep.in):

type         2 Si O
version      4
cutoff       8 4
n_max        4 4
basis_size   8 8
l_max        4 2 0
neuron       30
lambda_e     1.0
lambda_f     1.0
lambda_v     0.1
batch        1000
population   50
generation   100000

• type 2 Si O
  • First integer = number of element types. Remaining = species names in the order they will appear everywhere downstream. This order is load-bearing: it must match train.xyz, test.xyz, the potential header, and the model.xyz used in the deployed GPUMD MD.
• version 4
  • NEP4 syntax. Default unless reproducing a legacy NEP3 fit.
• cutoff 8 4
  • Radial / angular cutoffs in Å. 8 4 is a robust starting point for most condensed-phase systems.
• n_max 4 4, basis_size 8 8, l_max 4 2 0, neuron 30
  • Descriptor and network complexity. Start here, only increase after dataset quality is confirmed.
• lambda_e, lambda_f, lambda_v
  • Loss weights on energy / force / virial. Set lambda_v 0 when the dataset has no virial labels or when the convention is uncertain.
• batch 1000, population 50, generation 100000
  • Training control. generation 5000 is only useful for a quick smoke test — it is almost never enough for convergence. Production fits typically need 50000–200000 generations. Start with 100000 and increase if the loss.out curve has not plateaued.

Step 3. Run training

nep

Outputs in the current directory:

• nep.txt — the trained potential
• nep.restart — restart state (needed for later fine-tuning)
• loss.out — training and validation losses per generation
• energy_train.out, energy_test.out — parity data
• force_train.out, force_test.out — parity data
• virial_train.out, virial_test.out — parity data (when virials used)

Monitor loss.out with the bundled helper:

python scripts/summarize_nep_loss.py loss.out

Step 4. Inspect parity and loss

Look at the final rows of loss.out:

• all losses should be finite (no NaN / inf)
• training loss should be lower than or comparable to validation loss — if validation is dramatically worse, the dataset coverage or labeling is suspect
• a good train parity with a bad test parity points to dataset gap

Quantitative guidance for diagnosing overfitting:

• If test RMSE is more than 2x the training RMSE for energy or force, suspect overfitting or a train/test distribution mismatch.
• If the loss curve shows training loss still decreasing but test loss increasing or plateauing for more than ~10% of total generations, training should be stopped or the model complexity reduced.
• Compare force RMSE against the physical force scale: for a bulk solid at equilibrium, force RMSE above 50 meV/Å typically indicates a problem; for liquids or reactive systems, 100–200 meV/Å may be acceptable.
• If energy RMSE per atom exceeds ~5 meV/atom for a well-behaved bulk system, check dataset consistency before increasing model complexity.

Compute parity metrics from the parity outputs:

python scripts/parity_from_nep_outputs.py --prefix energy_test
python scripts/parity_from_nep_outputs.py --prefix force_test

Step 5. Downstream validation

RMSE alone is not enough. Before calling the fit "done":

1. drop nep.txt into a GPUMD MD run at the target state
2. verify numerical stability in a short NVE segment (no runaway energy)
3. check at least one target-state observable (lattice constant, RDF, diffusion, conductivity, …)

If the short MD diverges but the parity plot looks fine, the model is not yet fit for production.

Step 6. Deploy into GPUMD

potential nep.txt

That's it at the syntax level — but see the GPUMD md subskill for the full deployment workflow.

Runtime-verified end-to-end example — PbTe (DFT → NEP)

This complete pipeline was runtime-verified on 2026-04-16 using a 250-atom PbTe supercell from GPUMD tutorial 11_NEP_potential_PbTe. It demonstrates the full DFT → format conversion → NEP training → loss plotting workflow.

Overview

train.xyz (GPUMD tutorial)
    │
    ▼  ASE: extract 1 frame, strip labels
POSCAR (250 atoms, Pb₁₂₅Te₁₂₅)
    │
    ▼  vaspkit: KPOINTS + POTCAR
    ▼  VASP static SCF (16 cores)
OUTCAR (E = −919.06 eV, forces, stress)
    │
    ▼  dpdata: vasp/outcar → extxyz
train.xyz (1 frame, energy + forces + virial)
    │
    ▼  nep (GPU, 2000 generations)
nep.txt + loss.out
    │
    ▼  matplotlib
loss_plot.png

Step 1: Extract structure

from ase.io import read, write
atoms = read('path/to/gpumd/train.xyz', index=0, format='extxyz')
atoms.calc = None; atoms.info = {}
atoms.arrays = {k: v for k, v in atoms.arrays.items()
                if k in ('numbers', 'positions')}
atoms = atoms[atoms.numbers.argsort()]
write('POSCAR', atoms, format='vasp', vasp5=True, sort=True)

Step 2: VASP static SCF

Generate inputs via vaspkit:

echo -e "102\n1\n0.04" | vaspkit   # → KPOINTS (Γ-only) + POTCAR (Pb_d + Te)

INCAR (low-precision test):

SYSTEM = PbTe pipeline test
ISTART = 0; ICHARG = 2
ENCUT  = 300          # above Pb_d ENMAX=237.8
PREC   = Normal; LREAL = Auto; EDIFF = 1E-4
NELM   = 40; ISMEAR = 0; SIGMA = 0.1
LWAVE = .FALSE.; LCHARG = .FALSE.; NCORE = 4

OMP_NUM_THREADS=1 mpirun -np 16 vasp_std > vasp.out 2> vasp.err

Result: converged in 15 DAV steps, E = −919.062 eV (−3.676 eV/atom).

Step 3: Convert to extxyz

import dpdata
ds = dpdata.LabeledSystem('OUTCAR', fmt='vasp/outcar')
ds.to('extxyz', 'train.xyz')
# → 1 frame, 250 atoms, energy + forces + virial

Step 4: NEP training

nep.in:

type          2 Pb Te
version       4
cutoff        8 4
n_max         4 4
basis_size    8 8
l_max         4 2 0
neuron        30
generation    2000
batch         1000
population    50
lambda_e      1.0
lambda_f      1.0
lambda_v      0.1

nep    # reads nep.in + train.xyz, writes nep.txt + loss.out

Result (RTX 4090, 35.4 s):

Metric	Value
Total loss	0.137
Energy RMSE	0.000 eV/atom (1 frame → perfect fit)
Force RMSE	0.068 eV/Å
L1 regularization	0.028
L2 regularization	0.036

Step 5: Plot loss curve

import matplotlib.pyplot as plt
import numpy as np

data = np.loadtxt('loss.out')
gen = data[:, 0]

fig, axes = plt.subplots(2, 2, figsize=(12, 9))
fig.suptitle('NEP Training Loss')

axes[0,0].semilogy(gen, data[:,1], 'b-'); axes[0,0].set_title('Total Loss')
axes[0,1].semilogy(gen, data[:,4], 'r-'); axes[0,1].set_title('Energy RMSE')
axes[1,0].semilogy(gen, data[:,5], 'g-'); axes[1,0].set_title('Force RMSE')
axes[1,1].semilogy(gen, data[:,2], 'm-', label='L1')
axes[1,1].semilogy(gen, data[:,3], 'c-', label='L2')
axes[1,1].set_title('Regularization'); axes[1,1].legend()

for ax in axes.flat:
    ax.set_xlabel('Generation'); ax.grid(True, alpha=0.3)
plt.tight_layout(); plt.savefig('loss_plot.png', dpi=150)

loss.out column reference

Col	Field	Description
0	generation	training step (logged every 100)
1	loss_total	total weighted loss
2	loss_L1	L1 regularization
3	loss_L2	L2 regularization
4	RMSE_e_train	energy RMSE on train (eV/atom)
5	RMSE_f_train	force RMSE on train (eV/Å)
6	RMSE_v_train	virial RMSE on train (eV/atom)
7	RMSE_e_test	energy RMSE on test (eV/atom)
8	RMSE_f_test	force RMSE on test (eV/Å)
9	RMSE_v_test	virial RMSE on test (eV/atom)

Notes on this single-frame test

• Energy RMSE = 0 is expected — one frame is trivially fit.
• Force RMSE = 0.068 eV/Å is acceptable for a pipeline test but too high for production. Real training needs hundreds of diverse frames.
• No test set was used (test.xyz absent), so test columns are all zero.
• This example validates the workflow plumbing, not the potential quality.

Key hyperparameters

Descriptor

Keyword	Description	Conservative default
version	NEP generation / syntax	4
cutoff	radial / angular cutoff in Å	8 4
n_max	radial / angular expansion depth	4 4
basis_size	radial / angular basis resolution	8 8
l_max	angular body complexity (3b / 4b / 5b)	4 2 0
neuron	hidden-layer width of the fitting network	30

Loss weights

Keyword	Target	Typical
lambda_e	energy	1.0
lambda_f	force	1.0
lambda_v	virial	0.1 (or 0 if no virial data)

Training control

Keyword	Description	Typical
batch	batch size	1000
population	evolutionary population	50
generation	total generations	5000-200000
zbl	ZBL short-range cap	0=off, 1=flexible, 2=fixed universal

zbl values: ZBL adds a universal repulsive core for close-range encounters. Use 0 (off) for well-sampled equilibrium systems. Use 1 for a flexible short-range correction fitted during training. Use 2 for the fixed Ziegler-Biersack-Littmark universal potential — recommended for reactive or radiation-damage datasets where atoms may approach very closely but training data may not fully cover those configurations.

Conservative tuning order

1. fix dataset quality first (coverage, label consistency, species order)
2. confirm headers parse (validate_extxyz_headers.py)
3. train a baseline with the defaults above
4. inspect loss.out and parity
5. run a short MD sanity test
6. only then change descriptor complexity or loss weights

Skipping steps 1-5 and jumping straight to bigger neuron or l_max almost always makes the model larger without making it better.

Agent checklist

• train.xyz and test.xyz header-validated
• species order in type matches the dataset and any downstream GPUMD model.xyz
• units consistent (Å, eV, eV/Å)
• virial convention stated (or lambda_v 0)
• nep.in baseline matches conservative defaults
• loss.out free of NaN / inf
• short downstream MD sanity test passed
• nep.txt and nep.restart saved for later fine-tuning

Read first

• references/dataset-and-inputs.md
• references/training-evaluation-deployment.md

Read when needed:

• references/nep-keyword-cheatsheet.md
• references/labels-from-dft.md
• references/auxiliary-properties.md

Bundled templates and helpers

• assets/examples/baseline/nep.in
• assets/examples/baseline/train.xyz
• assets/examples/baseline/test.xyz
• validate_extxyz_headers.py
• scripts/summarize_nep_loss.py
• scripts/split_train_test.py
• scripts/parity_from_nep_outputs.py

References

• NEP documentation: https://gpumd.org/nep/index.html
• nep.in: https://gpumd.org/nep/input_files/nep_in.html
• train.xyz / test.xyz: https://gpumd.org/nep/input_files/train_test_xyz.html
• GPUMD-Tutorials: 11_NEP_potential_PbTe